Data Scientist, Planning and Shipping Optimization, GCF ACES Central - Nashville, United States - Amazon

Description

The Global Customer Fulfillment (GCF) Amazon Customer Excellence System (ACES) team is seeking a Data Scientist to define and model volume availability from a Fulfillment Center perspective, as in what is the ideal assignment curve to feed FCs in order to optimize network throughput, labor, transportation, speed, and quality (DEA).

In this role, you will have to investigate the impacts of customer ordering behaviors, systemic flows/holds, and capacity misalignments have on our ability to execute customer promises precisely (not early and not late) at the lowest end-to-end network cost.

This role calls for an individual who is technically proficient as well as operationally sound in order to gain credibility and align methodologies with scientists, program managers, product managers, fulfillment leadership, and key stake holders at various levels of the organization.

The Data Scientist will build the foundational data pipelines and data tables, analyze large data sets, automate solutions to constantly prove and disprove hypotheses.

This is a hands-on role which requires someone that is motivated to roll up their sleeves and become a builder themselves when needed.

Our ideal candidate thrives in a fast-paced environment, relishes working with big data, enjoys the challenge of highly complex business problems (that are typically being defined in real-time), and, above all, is a passionate about data science and machine learning.

Key job responsibilities

• Research and develop new methodologies for scalable demand forecasting and resource utilization.
• Improve upon existing methodologies and implement model enhancements.
• Manipulate/data mine from databases (Redshift, SQL Server, Oracle DW) and create automated metrics using complex databases.
• Collaborate with BI/Data Engineer teams and drive the collection of new data and the refinement of existing data sources to continually improve data quality.
• Build automated tools and support structures needed to analyze data, design metrics for complex systems.
• Create and track accuracy and performance metrics (both technical and business metrics).
• Create, enhance, and maintain technical documentation, and present to other scientists, engineers and business leaders.
• Understanding how to thrive in ambiguous environments and lead other teams without line of sight to a clear solution in the short term

A qualified candidate must have demonstrated ability to develop and manage medium to large-scale models and methodologies that are statistically grounded but also functional and practical.

Must possess strong written and verbal communication skills, proven ability to engage and collaborate with customers to drive improvements. Possess high intellectual curiosity with ability to quickly learn new concepts/frameworks, algorithms and technology.

About the team

The GCF ACES Network Flow Optimization and Risk Mitigation (nFORM) team operates across our global planning systems (SCOT FO/F2P/ATROPS) and local FC Amazon Fulfillment Technologies (AFT) to maximize the utilization of the end-to-end supply chain (FC, SC, Air, ATS, and AMZL) to deliver on speed, cost, and quality for our customers.

Our primary purpose is to transform and influence shipment flows from order to customer doorstep by ensuring that global and local planning engines have the right granularity of data to allow Fulfillment Centers to optimize performance while adhering to the networks flow plan while minimizing risk.

We are open to hiring candidates to work out of one of the following locations:

Arlington, VA, USA | Bellevue, WA, USA | Nashville, TN, USA

Basic Qualifications

2+ years of data scientist experience

3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience

3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience

Master's degree in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field

Preferred Qualifications

Experience in Python, Perl, or another scripting language

Experience in a ML or data scientist role with a large technology company

Experience with data modeling, warehousing and building ETL pipelines

Amazon is committed to a diverse and inclusive workplace.

Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

For individuals with disabilities who would like to request an accommodation, please visit

Our compensation reflects the cost of labor across several US geographic markets.

The base pay for this position ranges from $111,600/year in our lowest geographic market up to $212,800/year in our highest geographic market.

Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience.

Amazon is a total compensation company.

Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits.

For more information, please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.